Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

observed that direct change-from-baseline impedance measurements 12 and a multiplexed model built of absolute and impedance-change data appeared to have exceptional performances 9 .
In light of our previous results in a porcine model, we undertook evaluation of this same approach in a human model.An established method to safely mimic progression toward hemodynamic instability in humans is through use of a lower body negative pressure (LBNP) model 4,[13][14][15] .The application of LBNP results in "a central volume shift to the lower body, which creates central hemodynamic conditions that may mimic those obtained during actual blood loss" 4 .LBNP has been used to evaluate various diagnostic signals that may assist in earlier detection of hemorrhage 8,13,[16][17][18][19] .Absolute and relative vital sign and electrical impedance-based metrics are evaluated via direct analysis and via a machine-learning (ML) based approach.

Subjects
Eighteen healthy male and female volunteers (11 males, 7 females) with an average (± standard deviation) age of 32.7 ± 8.2 (23-51), height of 172 ± 8.6 cm (158-193 cm), weight of 77.1 ± 10.8 kg (63-101.8kg), and body mass index of 25.7 ± 2.6 kg/m 2 (21.8-30 kg/m 2 ) were recruited to participate in this LBNP study (January to March 2020).Figure 1 provides an overview of the measurements recorded on one subject.The study was originally powered for 25 subjects, but was stopped early because of COVID.Of the 18 subjects recruited for the study, there were impedance instrumentation issues on 2 subjects.Thus, we restricted the analysis to the 16 subjects where complete data was available.LBNP levels were increased in a step-wise manner (15 mmHg per step, held stable for 5 min per step) until a clinically evident hypotensive state developed.The average LBNP reached over all subjects was 80.0 ± 11.7 mmHg (60-100 mmHg), with 2 subjects stopping at 60 mmHg and one subject continuing to 100 mmHg.
The absolute and relative data are displayed as box-and-whisker plots (25-75% with N = 16 samples for each) in Figs. 2 and 3. Note that Pleth A is a metric calculated from the Pleth waveform (see "Methods" section) and EIT and EIS metrics are average, filtered impedance data corresponding to the noted sites (thx = thorax, ab = abdomen).Minute-based averages of all the data is additionally shown in Supplementary Appendix A.1.For the absolute metrics, a red square indicates significant differences between the current LBNP level and baseline (p < 0.05), and a green star indicates significant differences between consecutive LBNP levels (p < 0.05).The relative metrics in Fig. 3 show the change in metric values over the 5-min baseline period (0 mmHg) and across consecutive LBNP levels (0-to-15, 15-to-30, 30-to-45, and 45-to-60 mmHg).A description of the baseline-change calculation is provided in Supplementary Appendix A.2. Green stars in these plots indicate a significant difference between the metric changes across consecutive LBNP levels and baseline variability (p < 0.05).Bar graphs of area-under-the-curves (AUCs) are presented (Fig. 4A,B) for the absolute and relative metrics with overlaid 95% confidence intervals (CI), where AUCs are calculated from the receiver operating characteristic (ROC) curves.

Absolute metric analysis
The absolute metrics and associated AUCs are presented in Figs. 2 and 4A.HR and Pleth A trends appear to track with increasing LBNP, while only subtle trends appear in the remaining metrics (Fig. 2).However, there is significant overlap in the distributions between LBNP levels indicating a limited ability to differentiate states.
Figure 1.Photo of a subject with sensors attached prior to starting the LBNP experiment.The EIT belts (blue arrows) are positioned around the chest and abdomen, the EIS electrodes (orange) inject current across the thorax, abdomen, and arm, bioimpedance cardiography (BC) uses custom electrodes placed in prescribed locations above and below the heart, and 3 NIRS sensors recorded data in similar locations.Although not present in the photo (due to poor timing), an arterial line was placed on the patient's left arm to invasively measure MAP.
Only HR and Pleth A had significant differences; (1) with respect to baseline, starting at LBNP levels ranging from 30 mmHg (Pleth A ) to 45 mmHg (HR)-indicated by red squares, and (2) between consecutive LNBP levels from 45-to-60 for HR and 15-to-30 for Pleth A -indicated by green starts.The best AUCs were 0.66 (EIS ab ), 0.83 (Pleth A ), 0.89 (Pleth A ), and 0.97 (Pleth A ) for LBNP levels of 15, 30, 45, and 60 mmHg, respectively, where CIs are only above 0.8 for HR and Pleth A for an LBNP of 60 mmHg.Excluding HR and Pleth A , most of the AUCs in Fig. 4A are close to 0.5 (all are under 0.7), indicating a limited ability to differentiate states.The best AUC across impedance metrics is 0.68 (EIT thx ) with a CI of (0.50-0.84) at an LBNP level of 60 mmHg.The EIS data shown here correspond to the impedance magnitude and frequency that yields the maximum AUC.Specifically, the best frequencies were all less than or equal to 560 Hz, except for the comparison of 30 mmHg to baseline for the thorax (26.5 kHz).One could confidently say that hypovolemia induced via the LBNP model remains subclinical to vital sign metrics for an LBNP of 15 mmHg and perhaps up to 30 mmHg, although Pleth A shows significant differences with an AUC of 0.83 (CI 0.59-0.96).

Relative metric analysis
The relative metrics and associated AUCs are presented in Figs. 3 and 4B.Overall, the relative metrics appear very good at differentiating between increasing levels of hypovolemia (i.e., change between consecutive LBNP levels), with HR and EIT/EIS measurements recorded from the thorax and abdomen showing significant differences (green stars in Fig. 3).No metric has perfect separation from the baseline variability, but the EIT thorax data, EIT thx , comes closest, achieving AUCs of 0.99, 0.98, 0.97, and 0.97 for each LBNP level studied with CIs above 0.92 for LBNP changes of 0-to-15 mmHg and 45-to-60 mmHg.The EIT thx metric outperforms all other metrics (Fig. 4B) with a high degree of efficacy in identifying hypovolemia (AUC = 0.97, CI 0.95-1.00)across all levels of LBNP explored here.Some other techniques achieved AUCs > 0.9 for a subset of LBNP ranges: HR for LBNP change of 45-to-60 mmHg (0.93, CI 0.75-1.00),EIS on the thorax for LBNP changes of 0-to-15 (0.93, CI 0.79-0.99)and 45-to-60 mmHg (0.91, CI 0.71-1.00),and EIS on the abdomen for LBNP change of 45-to-60 mmHg (0.93, CI 0.78-1.00).The EIS data presented here correspond to the impedance magnitude and   www.nature.com/scientificreports/frequency that yields the maximum AUC.In general, these optimum frequencies fell between 8 and 27 kHz.

ML analysis: individual technologies
The ML approach relied on a binary time-series classification (TSC) method using a random forest model, 15-fold cross-validation, and its overall performance was assessed using out-of-sample diagnostic performance.The outof-sample AUC and F1 performance results for the binary TSC models using 7-min slope windows filtered for each LBNP change are shown in Fig. 6.F1 scores were used in addition to AUC to accurately measure the predictive performance of the ML models; unlike the balanced data set used in the above absolute and relative metric analyses, the ML data set often contained imbalanced data with more hypovolemic samples than normovolemic  samples, making F1 score a more apt performance metric 20

ML analysis: multi-technology
In addition to measuring the out-of-sample predictive performance of each individual technology, each model's predictions were combined and used in a majority voting scheme to measure the predictive performance of combined technologies.Combining traditional vital sign technologies (Fused Vitals ) yielded F1 scores of 0.60, 0.76, 0.80 and 0.79 for the 0-to-15 mmHg, 15-to-30 mmHg, 30-to-45 mmHg, and 45-to-60 mmHg LBNP changes, respectively.Combining impedance-based technologies (Fused EIT,EIS ) yielded F1 scores of 0.78, 0.82, 0.87 and 0.86 for each LBNP change respectively.Combining all non-invasive technologies (Fused All ) yielded mean F1 scores of 0.90, 0.92, 0.94, and 0.93 for each LBNP change respectively, far outperforming all individual technologies and other multi-technology models (see last 3 sets of results in Fig. 6).

Discussion
We have previously described the high performance of non-invasive impedance-based measurements in the early identification of occult hemorrhage in a porcine model 12 and the ability of machine learning to improve this performance through the application of multivariable algorithms 9 .We have now extended this observation into an established human model of LBNP progressive circulatory instability from central hypovolemia.The present investigation evaluated the performance of multiple technologies to detect early subclinical changes in LBNP in an absolute and relative (change) sense compared to standard vital sign metrics.Impedance-based non-invasive sensing performed best when used as relative metrics.The performance of relative EIT thx is worth noting.It had an AUC of 0.99 (CI 0.95-1.00)at the smallest LBNP change (0-to-15 mmHg) and when requiring 100% sensitivity it yielded an extremely high specificity of 87.5% especially when compared to the 25% specificity achieved by the best vital sign metric (MAP) at this LBNP change.LBNP 0-to-15 mmHg is almost always clinically occult and existing detection solutions perform poorly at this LBNP level with high rates of false negatives.Given these encouraging results, it is important to highlight the clinical value of analyzing relative metrics.Based on Ref. 14 , LBNP levels of 15, 30, 45, and 60 mmHg respectively correspond to blood losses of 93, 193, 313, and 451 ml, and consequently, for LBNP levels changes of 0-to-15, 15-to-30, 30-to-45, and 45-to-60 mmHg there should be a corresponding increased blood volume loss of 93, 100, 120, and 138 ml.Thus, the relative metric-based analysis essentially evaluates if one can detect an increase in blood loss relative to no blood loss for these particular volumes.The data here suggests that a simple threshold applied to the change in EIT thx is sufficient to detect these small occult-level blood volume changes (note that EIT thx change data are all above zero in Fig. 3), i.e. no apparent baseline (healthy state) is needed in the relative metric-based analysis.At the same time, Fig. 3 shows that the change in the EITthx metric cannot determine the severity of the bleed, i.e. all changes evaluated (0-to-15 up to 45-to-60) appear essentially the same.Although not considered here, it would be interesting, in a larger study, to investigate if any combination of absolute or relative metrics using ML techniques could help determine the severity of the bleeds in terms of cumulative volume or rate.
Similar to previous investigations 12 , we found that traditional vital signs measured with standard monitors such as ECG and plethysmography performed poorly in early detection of LBNP progression.EIT outperformed these metrics, with an observed AUC of 0.99 (CI 0.95-1.00)for EIT thx , while the best clinical metric at the same LBNP level exhibited an AUC of only 0.60 (CI 0.38-0.79)(HR).
As in our earlier porcine study, impedance performs poorly as an absolute metric (e.g., see Table 3 in Ref. 12 ).This can largely be attributed to the variation in body size and habitus between study participants.Given similar tissue impedance characteristics of all subjects (e.g., assuming muscle or adipose impedance is similar between subjects), a large subject will have a larger impedance compared to a small subject due to variations in electrode spacing and underlying constituent tissues.Unfortunately, simple calibration factors are generally not adequate, as the impedance is dependent on not only the subject's size (cross-sectional area), but also tissue composition (e.g., percent adipose tissue) 21 , hydration status (electrolyte content) 22 , and recent activity levels 23 .ROC analysis of EIT metrics including various height, weight, body mass index (BMI), and body surface area (BSA) factors were evaluated; no factor explored yielded notable improvements in the absolute analysis.
With respect to out-of-sample predictive performance, a clear contrast emerges between the strong outof-sample performance achieved using data generated by the impedance-based technologies as compared to the other technologies.Only impedance-based technologies were able to achieve AUCs > 90% across all four consecutive LBNP levels (see Fig. 6A).Mean AUC and F1 scores for impedance-based technologies dominated scores from all other individual technologies for the 0-to-15 mmHg LBNP change.HRV models tended to prioritize specificity over precision (i.e., do not flag subjects as hypovolemic unless they are very certain), resulting in strong AUCs, but weaker F1 scores.
Pleth-based results from Convertino and others, e.g. 8,18,19, appear promising but may have limitations when applied broadly.For example, investigators have established that in LBNP studies their Pleth-based metric can be separated into subjects that have high and low tolerance to hypovolemia 15,17 .This may impair detection of blood loss in "high tolerance" individuals.As we hypothesize that impedance is a measure of the sensitivity to the blood loss and not to the body's response to the blood loss, our impedance-based technology might not be susceptible to false-negative findings in subgroups of subjects.
The near perfect performance of EIT for the initial 0-15 mmHg step is particularly encouraging.This early period in the transfer of circulating blood volume is generally not detectable clinically and or with alternative existing technologies (Fig. 5A).A clinical alarm that activated just after initial hemorrhage would give clinicians the largest possible window for evaluation and treatment before onset of frank deterioration.
A limitation of the study was the small sample size.A larger study could have allowed for additional sub-analysis investigating dependencies on factors such as age, sex, height, and BMI.In terms of non-impedance related metrics, other studies have performed these types of analyses, e.g.see 24 for a review.If the performance described here is replicated in larger, future studies, development of a clinical early detection alarm system seems possible as these results indicate that low rates of false negative and false positive may be possible even in heterogenous clinical populations.A future wearable system may be reduced to a belt of electrodes and a small wireless device.
There are a number of limitations to the results reported here.While LBNP may be among the best available mimics for early hemorrhage it has all the limitations of a model system, and performance in actual clinical setting may be inferior.LBNP subjects in this study are all healthy volunteers.Patients will have significant phenotypic heterogeneity, may be taking medications that alter physiologic responses, and will likely be receiving intravenous fluids at variable rates 25 .A shortcoming of the ML-based approach is the reliance on calculating slopes of metrics from an overall LBNP-step-based experiment (5 min at each level).The slope calculations allowed for a large-enough database for ML training, but do not quite match the stepped, physical scenario of the LBNP model.However, the slope-based approach is how we envision the technology being implemented in practice.One additional limitation associated with the length of time-windows used for electrical impedance-based metric calculations should be noted.While the performance observed is exceptional using long time-windows (5-min here and 1-min in Ref. 12 ), for shorter duration windows, one might expect respiratory and cardiac-related signals to reduce the performance as these signals will no longer be removed through the averaging operation.Window length should continue to be considered as the technology is further developed.

Conclusions
Impedance-based noninvasive technologies appear to have promising diagnostic performance in the early detection of hypovolemia in a LBNP model.This is especially true when evaluated as a change with respect to baseline, and when incorporated in a multivariate machine learning algorithm.

Methods
A previously described step protocol for LBNP 14 was used to model hypovolemic cardiovascular instability in a cohort of healthy subjects.Prior to the study day, all subjects provided written informed consent after all procedures and risks of the study were fully explained, and the study was approved by Mayo Clinic's Institutional Review Board.All human research was performed in accordance with relevant guidelines/regulations.Subjects were continuously monitored during the experiment via observation and vital signs to ensure safety.

Experimental design
Subjects were instrumented with ECG and plethysmography for non-invasive vital sign monitoring, and a brachial arterial line was invasively introduced to capture mean arterial pressure (MAP).Non-invasive test devices included electrical impedance tomography (EIT) (SenTec AG, Landquart, Switzerland), electrical impedance spectroscopy (EIS) (Sciospec Scientific Instruments GmbH, Germany), Near infrared spectroscopy (NIRS) (custom-device 9 ), and bioimpedance cardiography (BC) (Starling, Baxter, USA) as shown in Fig. 1.Only vital sign metrics and EIT/EIS data are presented in this report.High subject-to-subject variability with the particular NIRS configuration and its coupling to the subject skin limited its value, and BC did not provide additional value relative to past reports of its performance in LBNP studies (see Supplementary Appendix A.4) 13 .The impedance spectroscopy device (EIS) recorded electrical impedance spectra from 3 sites (chest, abdomen, and arm), and the impedance tomography device (EIT) collected multiple impedance measurements from two 16-electrode belts positioned around the subjects' chest and abdomen.The belt was placed just below the nipple line (see Fig. 1), so belt placement was essentially the same across sexes.The pressure waveforms/values, ECG voltages, Pleth voltages, and LBNP levels were captured using LabChart 7.0 (ADI, Sydney, Australia).
Prior to LBNP progression each subject rested in the supine position for approximately 5 min.The LBNP levels proceeded as follows: baseline, 15 mmHg, 30 mmHg, 45 mmHg, 60 mmHg, 70 mmHg, 80 mmHg, 90 mmHg, and 100 mmHg.The experiment aimed to hold the negative pressure stable for 5 min at each level.There was a necessary transition period, which occurred within each 5-min interval.Consequently, the average stable period of each level was 4.2 min, i.e. ~ 48 s transition periods.At each 5-min time-point the LBNP moved to the next level.Each subject underwent an LBNP progression from a baseline hemodynamic state until a clinically evident hypotensive state developed.The target hypotensive state was achieved when any of the following criteria was met: (a) systolic BP of ≤ 70 mmHg or (b) the subject asked to stop because of symptoms typically including, but not limited to, lightheadedness or nausea.

Data
The collected data was averaged during the stable periods of each LBNP level.Only the data associated with LBNP levels of 0, 15, 30, 45, and 60 mmHg were considered-to focus on clinically occult states.The different LBNP levels are referred to as hypovolemia states as they are meant to mimic a volume contraction secondary to processes such as hemorrhage.
Baseline data was collected for each subject for five minutes before the LBNP process was initiated (LBNP = 0 mmHg), and the protocol took between 28 and 54 min.The beat-to-beat HR was calculated from R-peaks extracted from the ECG data.Because only a single channel of Pleth data was available for some subjects,

Figure 2 .
Figure 2. Absolute metric values displayed via quartile box-and-whisker plots.Black dots indicate any subject outside of the quartiles and the median value is shown as a black line segment.Red squares indicate significant differences between the given LBNP level and baseline.

Figure 3 .
Figure 3. Relative metric value displayed via quartile box-and-whisker plots.Black dots indicate any subject outside of the quartiles and the median value is shown as a black line segment.Green stars indicate significant differences between consecutive LBNP levels.

Figure 4 .
Figure 4. AUCs (bars) and 95% confidence intervals (whiskers) of the (A) absolute and (B) relative analysis for the vital signs and EIT/EIS metrics at each LBNP level (A) or each LBNP change (B).All analyses compare metric values (at the specific LBNP level) or change in metric values (over the specified LBNP change) to baseline or baseline variability, respectively-i.e.these are bleed versus no-bleed analyses performed at different LBNP levels.No comparisons are performed between the different LBNP levels, e.g.comparisons between metric changes from LBNPs of 0-to-15 to those from LBNPs of 15-to-30 are not considered.

Figure 5 .
Figure 5. (A-D) ROC curves corresponding to MAP, HR, and the best metrics from EIT (thorax) and EIS (abdomen) for the relative analysis of change between consecutive LBNP levels and baseline change.

Figure 6 .
Figure 6.Out-of-sample random forest performance results (bars) and 95% confidence intervals for each 15-fold cross validation (whiskers) for models trained on 7-min slopes from vital signs, impedance, and fused metrics, shown for each LBNP change.Fused results represent performance of majority voting ensembles that combine vital sign metrics (Fused Vitals ), impedance metrics (Fused EIT,EIS ), and all metrics (Fused All ).All analyses compare slopes over the specified LBNP change to baseline-i.e. they perform bleed versus no-bleed analyses.No comparisons are performed between different LBNP levels, e.g.comparing slopes from LBNPs of 0-to-15 to those from LBNPs of 15-to-30 are not considered.
. Individual and fused results are shown for models trained on 7-min slopes because that window length yielded the best out-of-sample results (see Supplementary Appendix A.3 for performance of other time windows).The mean vital sign F1 scores for an LBNP change of 0-to-15 mmHg are 0.59, 0.54, 0.57, and 0.60 for MAP, HR, HRV, and Pleth A , respectively.The EIT and EIS technologies performed generally better than vital signs, yielding mean F1 scores of 0.76, 0.76, 0.71, 0.71, and 0.69 for EIT thx , EIT ab , EIS thx , EIS ab , and EIS arm , respectively.